Facial liveness detection

ABSTRACT

Approaches for processing captured image to detect facial portion of a user within the captured image are described. In an example, a facial image from the processed captured image may be derived. Based on the derived facial image, determining a set of specular features and texture-based feature vector. Based on the specular features and the texture-based feature vector, whether the facial image is of a facial substitute or not may be ascertained.

TECHNICAL FIELD

The present subject matter relates to approaches for liveness detectionof a face being captured within an image for purposes of authenticationand identification.

BACKGROUND

Access to computing devices may be permitted on the basis of anauthentication mechanism. Modern devices have now begun utilizing facialrecognition as one such authentication mechanisms. In such a case, thecomputing device may be positioned with respect to the face of theauthorized individual. Based on the identification of the user, accessto the computing device may be permitted. In certain cases, individualsother than the authorized users may seek to circumvent suchauthentication mechanisms by using an image, such as physicalphotograph, electronic image, etc., of the authorized user to spoof thedevice into permitting access. Hence it is important for authenticationsystems to distinguish between the actual user and one pretending to bethe user. To such an end, appropriate authentication measures need to beimplemented to prevent spoofing.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. It should be noted that the description and figures are merelyexamples of the present subject matter and are not meant to representthe subject matter itself:

FIG. 1 is a diagram of an example setup in which the system detects uservia facial detection module.

FIG. 2 is a block diagram of an example facial detection system fordetecting facial portion of image.

FIG. 3 provides an illustrative graphs pertaining to various featurespertaining to the facial portion of the image, as per one example of thepresent subject matter.

FIG. 4 is a flow diagram outlining the method for the face detection.

Throughout the drawings, identical reference numbers designate similar,but not necessarily identical, elements. The figures are not necessarilyto scale, and the size of some parts may be exaggerated to more clearlyillustrate the example shown. Moreover, the drawings provide examplesand/or implementations consistent with the description; however, thedescription is not limited to the examples and/or implementationsprovided in the drawings

DETAILED DESCRIPTION

In user verification system, a computing device may determine whether toallow user access computing devices based on the verificationinformation provided by the user. Verification method may include,either be through a username and password, or may be based onauthentication of a biometric input provided by user. Examples of suchbiometric information may include, but not limited to, fingerprint, faceof user, iris scan and such.

With recent development in computing devices, facial recognitiontechnology is now being used to perform such authentications based onwhich access to the computing device may be permitted. Facialrecognition, amongst other aspects, provide a more robust mechanism forauthentication and is not subject to deficiencies of otherauthentication mechanism (such as loss of password, etc.). In addition,facial recognition provides ease in using the system wherein theauthentication as compared to password-based techniques.

Facial recognition-based authentication mechanism may also be prone totampering and may be compromised through spoofing using a photograph oran electronic image of the face of the authorized user. Photographs mayprobably be considered as the most common sources of spoofing attackssince they may be captured discretely without the knowledge of theauthorized user, or may even be obtained from social media platforms,where such images may be uploaded. To such an end, various techniquesexists which intend to detect liveness of the face based on which theauthentication is sought. Such mechanisms are able to ascertain whetherthe image being captured is that of another image or of the face of theauthorized user.

Generally, liveness detection of an image of the face may involvedetecting one or more physiological motions or indications, such asblinking of the eyes, eye movement, changes in facial expression, etc.However, such approaches require active involvement and efforts from theuser which may be discourage the user from relying on facialrecognition-based authentication. Other examples may involve performingauthentication based on other types of biometric inputs along withfacial recognition, but such approaches may have their own challengesand may tend to limit the effectivity of facial recognition as a primaryauthentication mechanism.

Approaches for detecting liveness of a face are described. As would beunderstood, liveness may be considered as a determination which is toascertain the facial capture being provided as a biometric inputcorrespond to the actual face of the authorized user and not based on animage of the authorized user. The present approaches provide anauthentication mechanism which is robust, computationally swift andnon-intrusive to detect face spoofing attacks which rely on recapturedimages of the face of an authorized user. In one example, an imageincluding a face of the authorized user is obtained. The obtained imagemay be further processed to identify portions of the image which mayinclude the face of a user. Once the portion of the image bearing theface of the user is determined, one or more specular reflectioncomponents may be determined. As would be understood, specularreflection components may be used to determine whether light beingcaptured by an image sensor is reflecting off a planar surface (e.g., aphotograph) or is reflected off from the face of a user. In addition tothe specular reflection components, one or more texture-based componentsmay also be determined based on the image under consideration. In anexample, the texture-based components may be represented through LocalBinary Patterns.

The specular reflection components and the texture-based components maybe further concatenated and further assessed to determine whether thefacial traits under consideration have been obtained from an actual faceof the user or have been obtained from an image of the face of the user.Once it is ascertained that the facial capture being provided are basedon actual face of the user, a further authentication may be performed todetermine whether the user is an authorized user or not. In an example,ascertaining whether the facial capture correspond to the face of theuser is performed through a computing device implementing a supportvector machine. It would be pertinent to note that determiningcomponents based on both specular reflection and texture-analysisprovide a computationally efficient and swift method for face livenessdetection.

These and other aspects are further described in conjunction with theaccompanying figures FIGS. 1-X. The above examples are further describedin conjunction with appended figures. It should be noted that thedescription and figures merely illustrate the principles of the presentsubject matter. It will thus be appreciated that various arrangementsthat embody the principles of the present subject matter, although notexplicitly described or shown herein, may be devised from thedescription and are included within its scope. Moreover, all statementsherein reciting principles, aspects, and examples of the present subjectmatter, as well as specific examples thereof, are intended to encompassequivalents thereof. In the figures, the left-most digit(s) of areference number identifies the figure in which the reference numberfirst appears. The same numbers are used throughout the figures toreference like features and components.

FIG. 1 illustrates an exemplary environment 100. The environment 100 mayfurther include a computing device 102 which implements facialrecognition for authenticating and verifying the identity of a user 104.As would be understood, the computing device 102 may include any devicecomprising a processor-based resource, with such a computing device 102being in a position to implement one or more functions based onexecution of plurality of instructions which may be stored within thememory of the computing device 102. Examples of such a computing device102 include, but are not limited to, laptops, mobile phones, palmtops,and tablet PCs. It may be noted that even though the present figure isdepicting the computing device 102 as a smartphone, any other examplemay be used without deviating from the scope of the present subjectmatter.

Returning to the present example, the computing device 102 may furtherinclude an image analysis module 106. Amongst other things, the imageanalysis module 106 may examine one or more facial capture which havebeen captured by the computing device 102. The image analysis module 106may then ascertain whether the facial capture so determined have beenobtained from a photograph of the user 104 or has been obtained from thephysical face of the user 104. To this end, the computing device 102 maycapture an image which includes an image of the face of the user 104.The image analysis module 106 may process the captured image to detectwhether any portion of the captured image includes a face. Once a faceis detected within the image, the portion of the image bearing the facemay be further processed to determine one or more specular features forthe image portion. In an example, the specular features may berepresented through a specular reflection gradient histogram.

While the specular features are being determined, the image analysismodule 106 may further process the image portion to determine one ormore texture-based features. In one example, the texture-based featuresmay be represented through a histogram generated based on techniquebased on Local Binary Patterns. Once determined, the image analysismodule 106 may concatenate the specular features and texture-basedfeatures may be further concatenated to provide a consolidated featureset. The consolidated feature set may be subsequently analyzed toascertain whether the facial capture obtained by the computing device102 is in fact from a photograph or from any other facial substitute. Inan example, the image analysis module 106 may ascertain whether thefacial capture thus obtained is of the actual face of the user 104 or isfrom another facial substitute of the user 104. It may be noted that thefacial substitute may include, but need not be limited to, a photograph,a digital image or video depicting the face of the user 104.

FIG. 2 describes the present subject matter in a more elaborated mannerin conjunction with FIG. 3. FIG. 2 illustrates a computing device 102 asper one example of the present subject matter. In the present example,the computing system 102 include processor(s) 202, memory 204, andinterface(s) 206. The processor(s) 202 may also be implemented as signalprocessor(s), state machine(s), and/or any other device or componentthat process image based on operational instructions. The interface(s)206 may include a variety of interfaces, for example, interfaces fordata input and output data. The computing device 102 may further includean image sensor(s) 208. The image sensor(s) 208 may be a series or anarray of sensors which may be used to capture either a still image,series of images or a video of the ambient environment towards which thecomputing device 102 may be directed towards. In an example, the imagesensor(s) 208 may be positioned on the body of the computing device 102in such a manner such that the face of the user 104 would be within thecapturing field of the image sensor(s) 208. The image sensor(s) 208 mayeither be integrated within the electronic circuitry of the computingdevice 102 (e.g., a primary camera or secondary camera of a smartphone)or may be externally coupled with the computing device 102 (e.g., webcamera coupled to a laptop or a desktop).

The interface(s) 206 may be such that they enable interconnection orcommunication of the computing device 102 with one or more otherdevices. The communication between such devices may be enabled through awired or a wireless network. The network may be a private network or apublic network and may be implemented as a wired network, a wirelessnetwork, or a combination of a wired and wireless network. The networkmay also include a collection of individual networks, interconnectedwith each other and functioning as a single large network, such as theInternet. Examples of such individual networks include, but are notlimited to, Global System for Mobile Communication (GSM) network,Universal Mobile Telecommunications System (UMTS) network, PersonalCommunications Service (PCS) network, Time Division Multiple Access(TDMA) network, Code Division Multiple Access (CDMA) network, NextGeneration Network (NGN), Public Switched Telephone Network (PSTN), LongTerm Evolution (LTE), and Integrated Services Digital Network (ISDN).

Returning to the present implementation, the computing device 102 mayfurther include module(s) 210 and data 212. The module(s) 210 may beimplemented as a combination of hardware and programming (e.g., programinstructions) to so implement one or more functionalities of themodule(s) 210. In one example, the module(s) 210 consists of imageanalysis module 106 and other module(s) 214. The data 212 may furtherinclude captured image 216, facial image 218, specular features 222,texture-based feature vector 224 and other data 226. The other data 224may include data that is either utilized by the module(s) 210 or mayinclude data that is generated by execution of such module(s) 210.

In relation to the operation of the computing device 102, a user, sayuser 104 may position the computing device 102 for initiating theauthentication process in order gain access to the computing device 102.In an example, the authentication process may be based on either aninput from a user or may be initiated automatically based on thedetection on action or gesture from the user 104. For example, theauthentication process may commence on detecting a specific motionexecuted by the computing device 102, which may typically occur when theuser 104 may move and hold the computing device 102 in front of theirface, in a vertical position.

Continuing with the present example, once the authentication process isinitiated, the image sensor(s) 208 may capture an image. The capturedimage may be stored within the computing device 102 as captured image216. The captured image 216 may be further processed by the imageanalysis module 106 to determine whether the captured image 216 includesa facial portion of an individual. The detection of the facial portionwithin the captured image 216 may be based on variety of techniques. Forexample, the detection of the facial portion from the captured image 216may be performed using edge detection technique. In another example, theportion of the image which includes the face may be determined based onmechanisms which employ a support vector machine.

Once it is determined that the captured image 216 includes a face, thecaptured image 216 may be cropped to isolate the portion which bears theface. The cropped image, stored as facial image 218, may be furtherprocessed before one or more features corresponding to the facial image218 are determined. In an example, the facial image 218 may be alignedand scaled. To this end, the image analysis module 106 may detect theposition of the eyes within the facial image 218. Once the position ofeyes is determined, the image analysis module 106 may provide a notionalline passing through the eyes of the facial image 218. The orientationof the facial image 218 may be further adjusted such that the notionalline passing through the eyes is aligned with respect to predefinedreference. Once aligned, the facial image 218 may be scaled such thatthe interocular separation between the eyes of the facial image 218 is apredefined value and that the eyes are at a predefined distance from thecertain points of references within the facial image 218. Once thescaling is performed, the facial image 218 may be resized to a definedsize. In an example, the facial image 218 may be resized to a 128*128RGB image.

Continuing with the present example, the image analysis module 106 mayfurther process the facial image 218 in order determine one or morespecular features pertaining to the facial image 218. As is generallyunderstood, specular features corresponding to an image for the samefacial construction differ when the image corresponds to an actual faceas opposed and when the image corresponds to a facial substitute (e.g.,a photograph) of the user. The difference typically arises since thespecular reflection from a flat surface (e.g., surface of a facialsubstitute) is different from the specular reflection which occurs fromthe contours of an actual or physical face.

In an example, for obtaining the specular features, the image analysismodule 106 may convert the facial image 218 (which is in the RGB colorspace) to the YUV color space. Once the conversion of the facial image218 is completed, the image analysis module 106 may separate the Ychannel corresponding to the facial image 218 in the YUV color space. Inanother example, the Y channel may be further separated and normalized.The image analysis module 106 may then perform a histogram normalizationonto the normalized Y channel of the facial image 218. As would beunderstood, histogram normalization may be considered to enhance finedetail within an image, such as the facial image 218. The normalized Ychannel may be further subjected to an intensity transformation functionto provide a transformed Y channel value. Thereafter, the image analysismodule 106 may subtract the transformed Y channel value from acorresponding normalized Y channel value which to obtain a series, whichmay then be collected into a 128-bin histogram to provide the specularfeatures in the form of a specular reflection gradient histogram 302. Itmay be noted that the present example is only illustrative and shouldnot be considered as limitation. Other approaches for determining thespecular features may also be relied. Such approaches would also bewithin the scope of the present subject matter. In one example, theimage analysis module 106 on determining the specular features may storethem as specular features 222.

With the determination of the specular features, one or moretexture-based features may then be obtained. It may be noted that thedetermination of the texture-based features is not dependent on thedetermination of the specular features. The texture-based features maybe determined prior to or in parallel with the determination of thespecular features, without deviating from the scope of the presentsubject matter.

Returning to the present example, the image analysis module 106 mayfurther process the facial image 218 to determine one or moretexture-based features. In an example, the image analysis module 106 mayrely on a local binary pattern-based algorithm for determining thetexture-based features. To this end, the image analysis module 106 mayretrieve the facial image 218 and covert into a grayscale image. Oncethe grayscale version of the facial image 218 is obtained, the imageanalysis module 106 may apply a local binary pattern (LBP) function ontothe grey-scaled facial image 218. As would be commonly understood, theLBP function when applied onto an image provide a LBP-based featurevector.

An LBP feature vector may be generated by initially dividing an image,e.g., the facial image 218, into a number of cell. For the purposes ofthe present description, a cell of about 3*3 pixels is determined. Thenumber of pixels in a cell may vary. Once the cell is determined, theimage analysis module 106 may further compare a central pixel value witheach of the neighbouring pixels. The comparison of the central pixelvalue with the each of the neighbouring pixel is captured by the imageanalysis module 106 as a binary value corresponding to the LBP cell. Forexample, a 1 may indicate that the central pixel value is greater thanthe neighbouring pixel value and a 0 may indicate the opposite. Based onthe binary value of the LBP cell, the image analysis module 106 maycalculate the LBP histogram 306. In an example, the LBP histogram 306may be normalized by the image analysis module 106. The LBP histogram306 for all cells composing the facial image 218 are then furtherconsolidated to provide the texture-based feature vector 224. It may beappreciated that the texture-based feature vector 224 determined throughLBP functions is only one of the many other possible examples that maybe utilized for determining the texture-based feature vector 224. Suchother approaches, too, fall within the scope of the present subjectmatter.

Once the specular features 222 and the texture-based feature vector 224are obtained, the image analysis module 106 may concatenate them toobtain a final feature vector 226. The final feature vector 226 may befurther assessed by the image analysis module 106 to determine whetherthe facial image 218 corresponds to an actual face or that it isobtained from another facial substitute of the user 104. In an example,the image analysis module 106 may rely on a support vector machineclassifier for performing the assessment based on the specular features222 and the texture-based feature vector 224.

On determining that the facial image 218 is based on a facial substituteof the user 104, the authentication process initiated by the computingdevice 102 may be terminated. However, on determining that the facialimage 218 does in fact correspond to an actual face of the user 104, afacial recognition may be performed to authenticate the identity of theuser 104. If the user 104 is determined to be an authorized user, accessto the computing device 102 may be provided.

The above described approaches may be used to determine whether thefacial image 218 under consideration is based on actual face of the user104 or has been obtained from another facial substitute of the user 104.As would be noted, the present approaches rely on specular features 222as well as texture-based feature vector 224 for the aforesaiddetermination.

FIG. 3 illustrate various plots and histogram profiles generated byprocessing the facial image 218. The image analysis module 106 mayprocess the facial image 218 to determine one or more texture-basedfeatures. In one example, the image analysis module 106 extract specularfeatures of the facial image 218 in the form of a specular reflectiongradient histogram 302. The specular reflection gradient histogram 302for all cells composing the facial image 218 are then furtherconsolidated to provide the specular features 304. In another example,image analysis module 106 relies on a local binary pattern-basedalgorithm for determining the texture-based features. The local binarypattern-based algorithm extracts the texture-based features of facialimage 218 in the form of LBP histogram 306. The LBP histogram 306pertaining to the facial image 218 is then normalized and stored astexture-based feature vector 308. Once the specular features 304 andtexture-based feature vector 308 are generated, both feature vectors arethen concatenated to form a final feature vector 226. The final featurevector 226 is further processed in order to detect whether to theliveness of the captured image. In one example, a classifier processesthe final feature vector 226 to ascertain whether the image captured isof a live person or not.

FIG. 4 illustrate example method 400, respectively, for ascertainingwhether a facial image corresponds to an actual face of the user orwhether it has been obtained from a facial substitute, according to anexample of the present subject matter. The order in which the methodsare described is not intended to be construed as a limitation, and anynumber of the described method blocks may be combined in any order toimplement the aforementioned methods, or an alternative method.Furthermore, method 400 may be implemented by processing resource orcomputing device(s) through any suitable hardware, non-transitorymachine-readable program instructions, or combination thereof, orthrough logical circuitry.

It may also be understood that method 400 may be performed by programmedand/or configured network devices present within a communicationnetwork, with such devices including the computing device 102 asdepicted in FIGS. 1-2. Furthermore, in certain circumstances, programinstructions stored in a non-transitory computer readable medium whenexecuted may implement method 400 through the respective devices, aswill be readily understood. The non-transitory computer readable mediummay include, for example, digital memories, magnetic storage media, suchas one or more magnetic disks and magnetic tapes, hard drives, oroptically readable digital data storage media. Although, the method 400are described below with reference to the computing device 102, asdescribed above, other suitable systems for the execution of thesemethods can be utilized. Additionally, implementation of these methodsis not limited to such examples

At block 402, an image may be captured in response to the initiation ofan authentication process. For example, once the authentication processis initiated, the image sensor(s) 208 may capture an image. The capturedimage may be stored within the computing device 102 as captured image216. In an example, the captured image 216 may include a facial portionof the user. The facial portion may be either the actual face of theuser 104 or may be from a facial substitute, e.g., a photograph.

At block 404, the captured image may be further processed to identify afacial portion. For example, the image analysis module 106 may processthe captured image to identify the portion of image which includes aface of an individual, such as the user 104. The image analysis module106 may identify the portion of the image which includes a face of theuser 104 based on either edge detection technique or based on mechanismswhich employ a support vector machine. Other approaches may also beemployed without deviating from the scope of the present subject matter.

At block 406, on detecting the presence of a face, the captured imagemay be cropped to obtain a facial image. In addition, as part ofpre-processing, the facial image may be further processed. For example,the image analysis module 106 may crop the captured image 216 may becropped to isolate the portion which bears the face. The cropped image,stored as facial image 218, may be further processed before one or morefeatures corresponding to the facial image 218 are determined.Thereafter, the facial image 218 may be aligned and scaled by detectingthe position of the eyes within the facial image 218. Once the positionof eyes is determined, the image analysis module 106 may provide anotional line passing through the eyes of the facial image 218, based onwhich the facial image 218 may be aligned. Thereafter, facial image 218may be scaled and resized to a predefined size. In an example, thefacial image 218 may be resized to a 128*128 RGB image.

At block 408, specular features corresponding to the facial image aredetermined. For example, the image analysis module 106 may furtherprocess the facial image 218 in order determine one or more specularfeatures pertaining to the facial image 218. The image analysis module106 may convert the facial image 218 to the YUV color space and mayseparate the Y channel corresponding to the facial image 218 in the YUVcolor space. The image analysis module 106 may then perform a histogramnormalization onto the normalized Y channel of the facial image 218. Thenormalized Y channel may be further subjected to an intensitytransformation function to provide a transformed Y channel value.Thereafter, the image analysis module 106 may subtract the transformed Ychannel value from a corresponding normalized Y channel value which toobtain a series, which may then be collected into a 128-bin histogram toprovide the specular features in the form of a specular reflectiongradient histogram.

At block 410, texture-based feature vector corresponding to the facialimage are determined. For example, the image analysis module 106 mayfurther process the facial image 218 to determine one or moretexture-based features. In an example, the image analysis module 106 mayrely on a local binary pattern-based algorithm for determining thetexture-based features. To this end, the image analysis module 106 mayretrieve the facial image 218 and covert into a grayscale image. Oncethe grayscale version of the facial image 218 is obtained, the imageanalysis module 106 may apply a local binary pattern (LBP) function ontothe grey-scaled facial image 218. As would be commonly understood, theLBP function when applied onto an image provide texture-based featurevector 224.

At block 412, the specular features and the texture-based feature vectorare concatenated to obtain a final feature vector. For example, theimage analysis module 106 may concatenate the specular features 222 andthe specular features to obtain a final feature vector 226.

At block 414, the final feature vector may be further assessed by theimage analysis module to determine whether the facial image correspondsto an actual face or that it is obtained from another facial substitute.For example, the image analysis module 106 may process the final featurevector 226 to determine whether the facial image 218 corresponds to anactual face or that it is obtained from another facial substitute of theuser 104. In one example, the image analysis module 106 may rely on asupport vector machine classifier for performing the assessment based onthe specular features 222 and the texture-based feature vector 224.Thereafter, the authentication process may continue and the user 104 maybe validated to ascertain whether access to the computing device 102 isto be provided or not.

Although examples for the present disclosure have been described inlanguage specific to structural features and/or methods, it is to beunderstood that the appended claims are not necessarily limited to thespecific features or methods described. Rather, the specific featuresand methods are disclosed and explained as examples of the presentdisclosure.

We claim:
 1. A system comprising: a processor; an image analysis modulecoupled to the processor, wherein the image analysis module is to:processing captured image to detect facial portion of a user within thecaptured image; deriving a facial image from the processed capturedimage; for the derived facial image, determining a set of specularfeatures and texture-based feature vector, wherein for determiningspecular features, the image analysis module is to: convert the facialimage to a YUV color space to obtain a converted facial image; andprocess the Y-channel of converted facial image to obtain the specularfeatures, wherein the processing of the Y-channel further comprises:normalizing the Y-channel; performing a histogram normalization of thenormalized Y-channel; further performing an intensity transformation onthe normalized Y-channel to obtain a transformed Y-channel; andsubtracting the transformed Y-channel from the corresponding normalizedY-channel to obtain the specular features, concatenating the specularfeatures and the texture-based feature vector to obtain a final featurevector; and based on the final feature vector, ascertaining whether thefacial image is of a facial substitute.
 2. The system as claimed inclaim 1, wherein the image analysis module to detect the facial portionof the user based on one of edge detection techniques and a supportvector machine classifier.
 3. The system as claimed in claim 1, whereinto derive the facial image from the processed captured image, the imageanalysis module is to: cropping a portion of the captured image bearingthe facial portion to obtain the facial image; detecting pair of eyeswithin the facial image; aligning the facial image with respect to anotional axis; and resizing the facial image to a predefined size. 4.The system as claimed in claim 1, wherein for determining thetexture-based feature vector, the image analysis module is to:converting the facial image to grayscale facial image; and obtaining thetexture-based feature vector based on applying a local binary pattern(LBP) function onto the grayscale facial image.
 5. The system as claimedin claim 4, wherein the applying the local binary pattern functionfurther comprises: for a cell comprising a predefined number of pixelsof the facial image, applying the LBP function; for a given cell,obtaining an LBP histogram based on the LBP function; normalizing theLBP histogram; and consolidating LBP histogram for all cells of thefacial image to obtain the texture-based feature vector.
 6. The systemas claimed in claim 1, wherein the ascertaining whether the facial imageis of a facial substitute based on the specular features and thetexture-based feature vector is based on a support vector classifierapplied onto the final feature vector.
 7. The system as claimed in claim1, wherein the captured image comprises one of a still image and a videostream.
 8. A method comprising: obtaining a captured image, wherein thecaptured image comprises a facial portion of a user; processing thecaptured image to detect the facial portion of the user within thecaptured image; generated a facial image from the processed capturedimage, wherein the facial image comprises the facial portion of theuser; for the generated facial image, determining a set of specularfeatures and texture-based feature vector, wherein determining the setof specular features comprises: converting the facial image to a YUVcolor space to obtain a converted facial image; and processing theY-channel of converted facial image to obtain the specular features,wherein the processing of the Y-channel further comprises: normalizingthe Y-channel; performing a histogram normalization of the normalizedY-channel; further performing an intensity transformation on thenormalized Y-channel to obtain a transformed Y-channel; and subtractingthe transformed Y-channel from the corresponding normalized Y-channel toobtain the specular features, concatenating the specular features andthe texture-based feature vector to obtain a final set of featurevector; and ascertaining whether the facial image corresponds to anactual face of the user based on the final set of feature vector.
 9. Themethod as claimed in claim 8, wherein the captured image in an RGBformat and is one of a still image and a video stream.
 10. The method asclaimed in claim 8, wherein generating the facial image comprises:cropping a portion of the captured image bearing the facial portion toobtain the facial image; aligning the facial image with respect to anotional axis based on detecting an angle of a notional line betweeneyes within the facial image with respect to the notional axis; andresizing the facial image to a predefined size.
 11. The method asclaimed in claim 8, wherein for determining the texture-based featurevector, the method further comprises: converting the facial image tograyscale facial image; and obtaining the texture-based feature vectorbased on applying a local binary pattern (LBP) function onto thegrayscale facial image.
 12. The method as claimed in claim 8, whereinthe ascertaining whether the facial image corresponds to an actual faceof the user based on the final set of feature vector is based on asupport vector classifier.